A Multiagent Decision-Making Model for Martian Landing Site Selection
Selecting suitable landing sites on Mars remains one of the key challenges due to the complex terrain of Mars and the diverse scientific objectives in Mars exploration missions. Landing site selection is inherently a multiobjective optimization problem that requires balancing scientific value and en...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11080353/ |
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| author | Rong Wang Yongjiu Feng Panli Tang Pengshuo Li Yiyan Dong Yusheng Xu Chao Wang Sicong Liu Yanmin Jin Shijie Liu Xiaohua Tong |
| author_facet | Rong Wang Yongjiu Feng Panli Tang Pengshuo Li Yiyan Dong Yusheng Xu Chao Wang Sicong Liu Yanmin Jin Shijie Liu Xiaohua Tong |
| author_sort | Rong Wang |
| collection | DOAJ |
| description | Selecting suitable landing sites on Mars remains one of the key challenges due to the complex terrain of Mars and the diverse scientific objectives in Mars exploration missions. Landing site selection is inherently a multiobjective optimization problem that requires balancing scientific value and engineering safety involving careful tradeoffs and strategic decision making. To address this challenge, we proposed a multiagent decision-making model for Martian landing site selection (MARS-MAS), which simulates the decision-making process of various participants in landing site selection. By modeling the interactions among <italic>managers</italic>, <italic>engineers</italic>, <italic>scientists</italic>, and <italic>evaluators</italic>, the MARS-MAS model integrates expert knowledge with intelligent optimization to effectively identify landing sites. We applied the MARS-MAS model to the ancient shoreline region on the eastern of Chryse Planitia and successfully identified three candidate landing ellipses: within McLaughlin Crater, north of Oyama Crater, and upstream of Mawrth Vallis. These areas not only possess significant geological and potential life detection value, but also show strong consistency with candidate sites from previous exploration missions, further validating the model’s effectiveness. The results highlight the advantages of the MARS-MAS model in providing an efficient, and intelligent solution for autonomous landing site selection on Mars and other extraterrestrial bodies, thereby enhancing decision-making capabilities for future deep space exploration missions. |
| format | Article |
| id | doaj-art-8b3ec3465a1c46f492cb7f7b1e2838ff |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-8b3ec3465a1c46f492cb7f7b1e2838ff2025-08-20T04:00:34ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118187431876110.1109/JSTARS.2025.358928811080353A Multiagent Decision-Making Model for Martian Landing Site SelectionRong Wang0Yongjiu Feng1https://orcid.org/0000-0001-8772-7218Panli Tang2Pengshuo Li3Yiyan Dong4Yusheng Xu5https://orcid.org/0000-0001-5571-7808Chao Wang6https://orcid.org/0000-0001-7565-2124Sicong Liu7https://orcid.org/0000-0003-1612-4844Yanmin Jin8https://orcid.org/0009-0002-7751-0458Shijie Liu9https://orcid.org/0000-0002-5941-0763Xiaohua Tong10https://orcid.org/0000-0002-1045-3797Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, ChinaShanghai Research Institute for Intelligent Autonomous Systems, Shanghai, ChinaCollege of Surveying and Geo-Informatics and the Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics and the Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics and the Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics and the Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics and the Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics and the Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration, Tongji University, Shanghai, ChinaCollege of Surveying and Geo-Informatics and the Shanghai Key Laboratory for Planetary Mapping and Remote Sensing for Deep Space Exploration, Tongji University, Shanghai, ChinaShanghai Research Institute for Intelligent Autonomous Systems, Shanghai, ChinaShanghai Research Institute for Intelligent Autonomous Systems, Shanghai, ChinaSelecting suitable landing sites on Mars remains one of the key challenges due to the complex terrain of Mars and the diverse scientific objectives in Mars exploration missions. Landing site selection is inherently a multiobjective optimization problem that requires balancing scientific value and engineering safety involving careful tradeoffs and strategic decision making. To address this challenge, we proposed a multiagent decision-making model for Martian landing site selection (MARS-MAS), which simulates the decision-making process of various participants in landing site selection. By modeling the interactions among <italic>managers</italic>, <italic>engineers</italic>, <italic>scientists</italic>, and <italic>evaluators</italic>, the MARS-MAS model integrates expert knowledge with intelligent optimization to effectively identify landing sites. We applied the MARS-MAS model to the ancient shoreline region on the eastern of Chryse Planitia and successfully identified three candidate landing ellipses: within McLaughlin Crater, north of Oyama Crater, and upstream of Mawrth Vallis. These areas not only possess significant geological and potential life detection value, but also show strong consistency with candidate sites from previous exploration missions, further validating the model’s effectiveness. The results highlight the advantages of the MARS-MAS model in providing an efficient, and intelligent solution for autonomous landing site selection on Mars and other extraterrestrial bodies, thereby enhancing decision-making capabilities for future deep space exploration missions.https://ieeexplore.ieee.org/document/11080353/Engineering safetylanding site selectionMarsmultiobjective optimizationmultiple decision agent |
| spellingShingle | Rong Wang Yongjiu Feng Panli Tang Pengshuo Li Yiyan Dong Yusheng Xu Chao Wang Sicong Liu Yanmin Jin Shijie Liu Xiaohua Tong A Multiagent Decision-Making Model for Martian Landing Site Selection IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Engineering safety landing site selection Mars multiobjective optimization multiple decision agent |
| title | A Multiagent Decision-Making Model for Martian Landing Site Selection |
| title_full | A Multiagent Decision-Making Model for Martian Landing Site Selection |
| title_fullStr | A Multiagent Decision-Making Model for Martian Landing Site Selection |
| title_full_unstemmed | A Multiagent Decision-Making Model for Martian Landing Site Selection |
| title_short | A Multiagent Decision-Making Model for Martian Landing Site Selection |
| title_sort | multiagent decision making model for martian landing site selection |
| topic | Engineering safety landing site selection Mars multiobjective optimization multiple decision agent |
| url | https://ieeexplore.ieee.org/document/11080353/ |
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